Salary Prediction in the IT Job Market with Few High-Dimensional Samples: A Spanish Case Study

The explosion of the Internet has deeply affected the labour market. Identifying most rewarded and demanded items in job offers is key for recruiters and candidates. This work analyses 4, 000 job offers from a Spanish IT recruitment portal. We conclude that (1) experience is more rewarded than education, (2) we identify five profile clusters based on required skills and (3) we develop an accurate salary-range classifier by using tree-based ensembles.

[1]  Silvia Corchs,et al.  Ensemble learning on visual and textual data for social image emotion classification , 2017, International Journal of Machine Learning and Cybernetics.

[2]  Yudong Zhang,et al.  A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy , 2016, Journal of Medical Systems.

[3]  A. Bhattacharya,et al.  Random Forest-Based Prospectivity Modelling of Greenfield Terrains Using Sparse Deposit Data: An Example from the Tanami Region, Western Australia , 2017, Natural Resources Research.

[4]  Gustavo Adolfo Alonso-Silverio,et al.  Simultaneous Gene Selection and Weighting in Nearest Neighbor Classifier for Gene Expression Data , 2017, IWBBIO.

[5]  Anna B. Holm.  E-recruitment: Towards an Ubiquitous Recruitment Process and Candidate Relationship Management , 2012 .

[6]  Vinod Chandran,et al.  Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry , 2017, Medicine and science in sports and exercise.

[7]  Phillip W. Braddy,et al.  E-recruitment and the benefits of organizational web appeal , 2008, Comput. Hum. Behav..

[8]  María Arrazola,et al.  Online job search in the Spanish labor market , 2014 .

[9]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[10]  Seong-Bae Park,et al.  PRICAI 2014: trends in artificial intelligence : 13th Pacific Rim international conference on artificial intelligence Gold Coast, QLD, Australia, December 1-5, 2014, proceedings , 2014 .

[11]  Richard A. Levine,et al.  Ensemble Learning for Estimating Individualized Treatment Effects in Student Success Studies , 2017, International Journal of Artificial Intelligence in Education.

[12]  Karthik Visweswariah,et al.  PROSPECT: a system for screening candidates for recruitment , 2010, CIKM.

[13]  A. Ng.  Feature selection, L1 vs. L2 regularization, and rotational invariance , 2004, Twenty-first international conference on Machine learning - ICML '04.

[14]  Giannis Tzimas,et al.  An Integrated e-Recruitment System for CV Ranking based on AHP , 2011, WEBIST.

[15]  Constantin Mang,et al.  Online Job Search and Matching Quality , 2012 .

[16]  Jie Xu,et al.  Multi-Class Support Vector Machine via Maximizing Multi-Class Margins , 2017, IJCAI.

[17]  Mehmet Fatih Amasyali,et al.  Locally adaptive k parameter selection for nearest neighbor classifier: one nearest cluster , 2017, Pattern Analysis and Applications.

[18]  A. Joy Christy,et al.  Novel Linear Regressive Classifier for the Diagnosis of Breast Cancer , 2017, 2017 World Congress on Computing and Communication Technologies (WCCCT).

[19]  Mitat Uysal,et al.  Ensemble approach for time series analysis in demand forecasting: Ensemble learning , 2017, 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA).

[20]  Shaha T. Al-Otaibi,et al.  A survey of job recommender systems , 2012 .

[21]  Giannis Tzimas,et al.  An Integrated E-Recruitment System for Automated Personality Mining and Applicant Ranking , 2012, Internet Res..

[22]  Huan Wang,et al.  A Job Recommender System Based on User Clustering , 2013, J. Comput..

[23]  Mauro Brunato,et al.  X-MIFS: Exact Mutual Information for feature selection , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[24]  Jean Paul Barddal,et al.  A Survey on Ensemble Learning for Data Stream Classification , 2017, ACM Comput. Surv..

[25]  A. Govardhan,et al.  Spatial Data Analysis Using Various Tree Classifiers Ensembled With AdaBoost Approach , 2017 .

[26]  Juan-Zi Li,et al.  Expert Finding in a Social Network , 2007, DASFAA.

[27]  Chen-Fu Chien,et al.  Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry , 2008, Expert Syst. Appl..

[28]  Jun Yue,et al.  Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier , 2016, Neural Computing and Applications.

[29]  Armelle Brun,et al.  Predict the Emergence: Application to Competencies in Job Offers , 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

[30]  M. Ekströma,et al.  Logistic regression for clustered data from environmental monitoring programs , 2018 .

[31]  J. Horton.  The Effects of Algorithmic Labor Market Recommendations: Evidence from a Field Experiment , 2015, Journal of Labor Economics.

[32]  Kush R. Varshney,et al.  Predicting employee expertise for talent management in the enterprise , 2014, KDD.

[33]  Jean-Philippe Malet,et al.  Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier , 2017 .

[34]  K. Ramar,et al.  Applicability of Clustering and Classification Algorithms for Recruitment Data Mining , 2010 .

[35]  Yao Liu,et al.  Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums , 2018, Decis. Support Syst..

[36]  Peter E.D. Love,et al.  Predicting Safety Risks in Deep Foundation Pits in Subway Infrastructure Projects: Support Vector Machine Approach , 2017, J. Comput. Civ. Eng..

[37]  Jing Tian,et al.  An ensemble learning-based fault diagnosis method for rotating machinery , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[38]  V. Senthil Kumaran,et al.  Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping (EXPERT) , 2013, Int. J. Metadata Semant. Ontologies.

[39]  George A. Tsihrintzis,et al.  A New Content-Based Recommendation Algorithm for Job Recruiting , 2015, SGAI Conf..

[40]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[41]  P. Kuhn,et al.  Is Internet Job Search Still Ineffective? , 2014, SSRN Electronic Journal.

[42]  Fabio Persia,et al.  Challenge: Processing web texts for classifying job offers , 2015, Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015).

[43]  Abdul Razak Hamdan,et al.  Knowledge Discovery Techniques for Talent Forecasting in Human Resource Application , 2009 .

[44]  B. Pham,et al.  Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods , 2017, Theoretical and Applied Climatology.

[45]  Alessandro Bozzon,et al.  Choosing the right crowd: expert finding in social networks , 2013, EDBT '13.

[46]  R. Schlögel,et al.  Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models , 2018 .

[47]  Roberto Battiti,et al.  Accelerated Backpropagation Learning: Two Optimization Methods , 1989, Complex Syst..

[48]  Sebastian Schemm,et al.  Nowcasting Foehn Wind Events Using the AdaBoost Machine Learning Algorithm , 2017 .